The impact of lexical and grammatical processing on generating code from natural language
Considering the seq2seq architecture of TranX for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architecture that relies on BERT encoder and a grammar-based decoder for which a formalization is provided. The paper highlights the importance of the lexical substitution component in the current natural language to code systems.
PDF Abstract Findings (ACL) 2022 PDF Findings (ACL) 2022 Abstract
Django
Methods
Adam •
Attention Dropout •
BERT •
Dense Connections •
Dropout •
GELU •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
LSTM •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Seq2Seq •
Sigmoid Activation •
Softmax •
Tanh Activation •
Weight Decay •
WordPiece
